Papers Multiple Instance Learning
“Multiple Instance Learning” 태그가 달린 논문 912편 · 필터 해제
Distribution-based deep multiple instance learning for tumor proportion scoring in NSCLC
Accurate assessment of tumor proportion score (TPS) in non-small cell lung cancer (NSCLC) is critical for treatment planning and prognosis. Key challenges include the tedious manual work required to annotate each slide, …
Multiple Instance LearningRe-mixing Embeddings for Patient Augmentation in Data Scarce Multiple Instance Learning
Data scarcity is a major bottleneck in medical Multiple Instance Learning (MIL), especially for rare diseases or expensive modalities. We introduce a statistically grounded patient augmentation approach that generates re…
Multiple Instance LearningFrom Point Estimates to Distributions: GMM Pooling for MIL in Preterm Birth Prediction
Preterm birth (PTB) prediction can enable targeted surveillance and timely intervention, yet most ultrasound-based models use a single selected transvaginal ultrasound (TVUS) frame per patient despite routine exams acqui…
Multiple Instance LearningBrain-Adapter: A Dual-Stream Vision-Language MIL Framework for Comprehensive 3D CT Diagnosis of Acute Intracranial Pathologies
Automated diagnosis of 3D brain CT scans is essential for critical care, yet it remains challenging due to the heavy reliance on manual annotations and the limited semantic understanding of conventional models. While 2D …
Multi-Label ClassificationMultiple Instance LearningQG-MIL: A Gated Transformer Aggregator for Domain-Agnostic Multiple Instance Learning in Medical Imaging
Attention-based Multiple Instance Learning aggregators in medical imaging are prone to attention concentration, producing overconfident and unstable predictions. We introduce QG-MIL, a gated transformer aggregator that a…
Multiple Instance LearningRaLMPH: Reliability-aware Learning for Multi-Pathologist Harmonization in Whole-Slide Image Classification
Multiple Instance Learning (MIL) is a standard paradigm for Whole-Slide Image (WSI) analysis and has achieved strong results in computational pathology. However, most MIL pipelines assume a single "gold" label per slide,…
Multiple Instance LearningImage ClassificationAGE-MIL: Anchor-Guided Evidence Learning for Patient-Level Prediction
Existing computational pathology methods predominantly operate within whole-slide image (WSI)-level multiple instance learning (MIL) paradigms, while patient-level modeling remains underexplored. In routine pathological …
Multiple Instance LearningFrom Patches to Patients: A study of the tile-to-slide performance transferability in Digital Pathology
Foundation Models (FMs) have recently redefined the state-of-the-art in histopathology by providing robust representations for whole-slide image (WSI) analysis. However, selecting the optimal foundation model (FM) for a …
Multiple Instance LearningGD-MIL: Grade-Disentangled Multiple Instance Learning for Multimodal Biochemical Recurrence Prediction in Prostate Cancer
Biochemical recurrence (BCR) after radical prostatectomy is a critical endpoint in prostate cancer, yet risk stratification relies almost entirely on variables dominated by Gleason grade. Whether H&E whole slide images (…
Multiple Instance LearningSelf-Supervised Vision Transformers for CBCT-Based Detection of Temporomandibular Joint Osteoarthritis
Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative condition whose osseous changes are often subtle on cone-beam CT (CBCT), making automated detection challenging. We study how well the DINO fami…
Multiple Instance LearningLRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification
Multiple instance learning (MIL) has become a standard paradigm for whole slide image (WSI) analysis in digital pathology, as it enables slide-level prediction without dense annotations. Existing MIL methods typically re…
Multiple Instance LearningKnowledge DistillationImage ClassificationDALE-CT: Depth-Aware Foundation Models for Computed Tomography
Recent breakthroughs in self-supervised learning (SSL), such as the Latent-Euclidean Joint-Embedding Predictive Architecture (LeJEPA), alongside successes in integrating visual encoders with language models, have driven …
Multiple Instance LearningSelf-Supervised LearningSymb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology
Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology. Existing methods primarily rely on heatmaps that highlight influential regions but do not ex…
Multiple Instance LearningIn-Context Multiple Instance Learning
Multiple Instance Learning (MIL) addresses problems where supervision is available at the level of bags of instances and has been successfully applied in fields ranging from computational pathology to satellite imagery. …
Multiple Instance LearningDo Foundation Models See Biology? Evaluating Attention Coherence with Spatial Transcriptomics in Glioblastoma
Whether attention maps from pathology foundation models capture genuine biology remains unknown, yet this question is critical for clinical trust and regulatory approval. We propose a spatial transcriptomics-based framew…
Multiple Instance LearningConveyance: A Versatile Framework for Learning in Structured Class Spaces
While machine learning (ML) architectures have evolved rapidly to account for complex data, loss functions like cross-entropy remain mostly structure-agnostic in many real-world applications. However, the "class-symmetri…
Multiple Instance LearningNormal Guidance is what Attention Needs
We consider training classifiers for 3D medical images using only one binary label for the entire volume rather than a label for each 2D slice. In such weakly supervised settings, can we learn accurate classifiers for sl…
Multiple Instance LearningAligning Cellular Sheaves with Classifier Attention for Interpretable Weakly-Supervised Pathology Localization
Weakly-supervised classification of whole-slide images with attention-based multiple instance learning (ABMIL) on top of foundation features now reaches near-saturation on Camelyon16 slide-level performance, but the corr…
Multiple Instance LearningThinking in Scales: Accelerating Gigapixel Pathology Image Analysis via Adaptive Continuous Reasoning
Traditional whole slide image (WSI) analysis methods typically rely on the multiple instance learning (MIL) paradigm, which extracts patch-level features at high magnification and aggregates them for slide-level predicti…
Multiple Instance LearningSpatial Blindness in Whole-Slide Multiple Instance Learning
Whole-slide MIL models are often called context-aware once graphs, Transform ers, or state-space modules are placed above patch embeddings. We show that this label can be deceptive. On pathology tasks where tissue archit…
Multiple Instance Learning